No more nested Ifs!

mutate(grade_group = ifelse(Grade >= 5 & Grade <= 8 & Subject == "ELA", "5-8", 
                            ifelse(Grade >= 5 & Grade <= 8 & Subject == "Math", "5-7", 
                                   ifelse(Grade < 5, "3-4", "Unknown")
                            )))

Correction 1 - multiple if statements, on their own:

mutate(grade_group = ifelse(Grade >= 5 & Grade <= 8 & Subject == "ELA", "5-8", NA),
       grade_group = ifelse(Grade >= 5 & Grade <= 8 & Subject == "Math", "5-7", grade_group),
       grade_group = ifelse(Grade < 5, "3-4", "grade_group"))

Correction 2 - translation table

grade_group <- tibble(grade = 3:8, 
                      ela_group = c("3-4", "3-4", rep("5-8", 4)), 
                      math_group = c("3-4", "3-4", rep("5-7", 4))
                      )
grade_group

## # A tibble: 6 x 3
##   grade ela_group math_group
##   <int>     <chr>      <chr>
## 1     3       3-4        3-4
## 2     4       3-4        3-4
## 3     5       5-8        5-7
## 4     6       5-8        5-7
## 5     7       5-8        5-7
## 6     8       5-8        5-7

results <- results %>% left_join(grade_group, by="grade")

group_by and mutate

Example: So I have this file with student profile info, but each student has one record per school year…so ying has 9th grade, 10th grade, 11th grade. i don’t trust that the demographics info in each year is identical, so i want to create a file with one row per student, taking the latest year

Option 1. Arrange by Student and desc(Year), then filter “First” of every student Option 2. Filter

df %>%
  group_by(student_id) %>% 
  mutate(max_year = max(school_year) %>% 
  filter(school_year == max_year) %>% 
  ungroup()

Group Max

df <- tibble(school = c("EBCS", "EBCS"), score = c(100, 50), teacher = c("x", "y"))
df %>%
  group_by(school) %>%
  mutate(max_score_by_school = max(score),
         best_teacher = map2_chr(score, teacher, ~.y[which.max(.x)])
  )

## Warning: package 'bindrcpp' was built under R version 3.3.3

## # A tibble: 2 x 5
## # Groups:   school [1]
##   school score teacher max_score_by_school best_teacher
##    <chr> <dbl>   <chr>               <dbl>        <chr>
## 1   EBCS   100       x                 100            x
## 2   EBCS    50       y                 100            y

mutate_at/summarise_at:

• Useful for when you need to apply the same function to many columns (i.e as.numeric())
• It will automotically mutate all your columns at once.
• You need to tell mutate which columns you want it to look at, and what to do with them.
• You can give it multiple columns and multiple functions. All functions will act on all columns.

mutate_at(vars(), funs())

Example

results %>%
  mutate_at(vars(score, grade), as.numeric)

mutate_if/summarise_if:

summarise_if(is.numeric, funs(mean, min, max, sd, n))
summarise_if(is.numeric, as.character)
summarise_if(is.numeric, as.factor)

verify(nrow(get_dupes(., student_id))==0)